Exploratory Data Analysis (EDA) automation project
The objective of using Pandas Profiling and SweetViz for Streamlined Exploratory Data Analysis (EDA) is to expedite the process of data quality assessment, visualization, and insights generation. By leveraging these libraries, the project aims to automate and simplify the EDA process, making it faster and more efficient.
Procedure and Steps:
Install Pandas Profiling and SweetViz:
- Use `pip install pandas-profiling sweetviz` to install both libraries.
Load Data and Perform EDA with Pandas Profiling:
- Use Pandas to load your dataset.
- Use Pandas Profiling to generate a comprehensive report on the dataset, including summary statistics, data types, missing values, and correlations.
Generate Visualizations with SweetViz:
- Use SweetViz to generate visualizations for better understanding of the dataset.
- SweetViz provides visualizations such as histograms, bar charts, scatter plots, and correlation matrices.
Interpret Results and Gain Insights:
- Analyze the Pandas Profiling report and SweetViz visualizations to identify patterns, outliers, and relationships in the data.
- Use these insights to make informed decisions about data cleaning, feature engineering, and modeling.
Tools Used:
- Pandas Profiling: A library for generating detailed EDA reports for a dataset.
- SweetViz: A library for generating visualizations to aid in EDA and data exploration.
10 MLOps Projects Ideas for beginners
Machine Learning Operations (MLOps) is a practice that aims to streamline the process of deploying machine learning models into production. It combines the principles of DevOps with the specific requirements of machine learning projects, ensuring that models are deployed quickly, reliably, and efficiently.
In this article, we will explore 10 MLOps project ideas that you can implement to improve your machine learning workflow.
MLOps Projects Ideas
- 1. MLOps Project Template Builder
- 2. Exploratory Data Analysis (EDA) automation project
- 3. Enhanced Project Tracking with Data Version Control (DVC)
- 4. Interpretable AI: Enhancing Model Transparency
- 5.Efficient ML Deployment: Accelerating Deployment with Docker and FastAPI
- 6. End-to-End ML Pipeline Orchestration: Streamlining MLOps with MLflow
- 7. Scalable ML Pipelines with Model Registries and Feature Stores
- 8. Big Data Exploration with Dask for Scalable Computing
- 9. Open-Source Chatbot Development with Rasa or Dialogflow
- 10. Serverless Framework Implementation with Apache OpenWhisk or OpenFaaS